Offloading among multiple radio access technologies (RATs) is an effective solution to tackle some of the key challenges faced by 5G wireless networks. While making an offloading decision, in user-centric offloading schemes, the network context is not taken into account, which limits the full utilization of the potential offered by these techniques.
In neuroscience, there is substantial evidence that suggests temporal filtering of stimulus by synaptic connections. In this paper, a novel frequency-dependent plasticity mechanism (FDSP) for neurocomputing applications is presented. It is proposed that synaptic junctions could be used to perform bandpass filtering on the input stimulus.
The spatial and temporal distribution of active users across a given region greatly affects the activity of base stations (BSs) and the cumulative interference in a wireless network. In this work, we propose a new approach for spatio-temporal analysis of wireless networks wherein stochastic geometry is used for the spatial domain and ON/OFF traffic models are used for the temporal domain.
The lysis-lysogeny switch in E. coli due to infection from lambda phage has been extensively studied and explained by scientists of molecular biology. The bacterium either survives with the viral strand of deoxyribonucleic acid (DNA) or dies producing hundreds of viruses for propagation of infection. Many proteins transcribed after infection by $\lambda $ phage take part in determining the fate of the bacterium, but two proteins that play a key role in this regard are the cI and cro dimers, which are transcribed off the viral DNA.
The increased penetration of different radio access technologies (RATs) and the growing trend towards their convergence necessitates the investigation of wireless heterogeneous networks (HetNets) from coverage and capacity perspective. This paper develops a unified framework for signal-to-interference-plus-noise ratio and rate coverage analysis of multi-RAT HetNets, with each RAT employing either a contention-free or a contention-based channel access strategy.
Recently, tools from stochastic geometry have gained much attention for modeling and analysis of dense cellular networks. Although, extensive studies are available in literature in this respect however, the approaches are generalized and often lack significance towards practical scenarios where network conditions vary dynamically. The main objective of this research is to provide some insights into the stochastic geometry based analysis of cellular networks, through suitable modifications, so that it can be used to estimate parameters of interest i.e., intensity and coverage probability for different tiers and scenarios under consideration.
Communication and computing shape up base for explosion of Internet of Things (IoT) era. Humans can efficiently control the devices around their environment as per requirements because of IoT, the communication between different devices brings more flexibility in surrounding. Useful data is also gathered from some of these devices to create Big Data; where, further analysis assist in making life easier by developing good business models corresponding to user needs, enhance scientific research, formulating weather prediction or monitoring systems and contributing in other relative fields as well. Thus, in this research a remotely deployable IoT enabled Wind Sonic Anemometer has been designed and deployed to calculate average wind speed, direction, and gust. The proposed design is remotely deployable, user-friendly, power efficient and cost-effective because of opted modules i.e., ultrasonic sensors, GSM module, and solar panel
There has been a growing interest and motivation in analog electronic circuit modeling of bio-cellular networks, which forms the basis of cellular functions of all living organisms. The complexity and size of such networks has made this task arduous, while opening up new opportunities as well. A number of modeling techniques, from mathematical models to computer simulations, have been used in this domain to aid the interpretation of such complex and sophisticated networks. This research article focuses on modeling of bio-cellular structures and processes on silicon substrate using transistors in analog domain.
Speech recognition in a signal with low SNR is a very challenging task. When the distance between the mic and the source is large, the mic records a mixture of Speech and Noise. This paper presents a Speech recognition system which performs Blind Source Separation using Degenerate Unmixing Estimation Technique to separate speech from noise. This system uses a Deep Recurrent Neural Network based method in order to achieve robust speech recognition. An experiment comparing the efficiency of the aforementioned system with an already established Speech Recognition system is presented at the end.
Recently, tools from stochastic geometry have gained much attention for modeling and analysis of dense cellular networks. Although, extensive studies are available in literature in this respect, the proposed approaches are generalized and often lack significance towards practical scenarios where network conditions vary dynamically. The main objective of this research is to provide some insights into the stochastic geometry based analysis of cellular networks, through suitable modifications, so that it can be used to estimate parameters of interest i.e., intensity and coverage probability for different tiers and scenarios under consideration. The main definition for probability of coverage has been redefined and complete closed form expression is derived. The intensity for different tiers have been estimated, with the help of proposed approach, beyond which no more gain in coverage probability can be achieved. Monte-Carlo simulations have been performed for validation of proposed approach and results are also compared with state-of-the-art approach.
Abstract Population coding has been established as the key mechanism for decoding sensory information received from periphery organs such as retinas and cochlear. In this paper a novel architecture is presented to embed population coding in neuromorphic hardware. A resonance based mechanism between two layers of neuron is utilized in the presented work. The mechanism discussed in this paper facilitates selective triggering of higher layer neurons which serves as target ensemble for evaluation of a particular input of interest. It has been shown that presented model can be used to detect any physical quantity (light intensity, temperature, etc.) feed to the sensor on the basis of population coding. Keywords Population coding; Neuromorphic hardware; Synaptic plasticity; Interspike interval; Tuning curve
nternet of Things (IoT) is a fast growing field and its purpose is to connect, objects with other objects or humans through internet and provide some useful data. Exploitation of data, gathered from objects of certain type, can help in making human life easier by making better decisions in business, weather, or any other field of study. Since weather is a very critical and important field of study and its real time analysis is really important for prediction of future trends. Therefore, in this research an IoT enabled ultrasonic anemometer has been designed which calculates wind speed & direction and uploads collected data to cloud. In depth analysis of equations for obtaining average wind speed and direction has been presented in this paper.
Introducing automation in different field of study by exploiting Internet of Things (IoT) technology has become a hot topic nowadays. In this research, a solution has been proposed for monitoring health of crops since automation in agricultural field initially requires monitoring. A centralised approach, without multi-hops, following a star topology has been used. The network constitute of two types of nodes i.e., sensor and gateway node. gateway node is equipped with GPRS module through data collected from sensor node is transferred to cloud in real-time. Five parameters have been monitored including ambient temperature & humidity, soil moisture, underground temperature, and light intensity. Testbeds has been designed and deployed at two different cites i.e., Civil department NED UET and Gadap Karachi, Pakistan. The results obtained in real time are satisfactory and can be used by researchers for introducing automation in agricultural sector.
Introducing automation in different field of study by exploiting Internet of Things (IoT) technology has become a hot topic nowadays. In this research, a solution has been proposed for monitoring health of crops since automation in agricultural field initially requires monitoring. A centralized approach, without multi-hops, following a star topology has been used. The network constitute of two types of nodes i.e., sensor and gateway node. gateway node is equipped with GPRS module through data collected from sensor node is transferred to cloud in real-time. Five parameters have been monitored including ambient temperature & humidity, soil moisture, underground temperature, and light intensity. Testbeds has been designed and deployed at two different cites i.e., Civil department NED UET and Gadap Karachi, Pakistan. The results obtained in real time are satisfactory and can be used by researchers for introducing automation in agricultural sector.
A large body of researchers across the globe has been moving genetically coded information and cellular processing techniques to the mathematical domain in terms of analogous variables and their equations. A recent trend has expanded this movement further into the realms of electronics. Many different attempts have been made to create electronic circuits and Boolean machines that model the internal wirings of genetic circuits. This paper presents a very well-known yet simple genetic circuit created to demonstrate bistability in the cellular system. It looks into the working of the Collins toggle switch (CTS), a synthetically created system of two repressors and their respective inducers and a reporter protein. The authors have created a finite state machine that is defined with variables analogous to the proteins in the CTS and responds to these variables in exactly the same way as the plasmids of CTS respond to the original proteins. The finite state machine analogue helps analyse the genetic circuit with greater depth and elaboration.
Warehouse is one of the most dangerous places to work because of many potential dangers. The accidents caused by heavy vehicles result in serious injuries and even death. This paper proposes a solution to this problem. When a worker happens to be in any of such hazardous situations inside or outside workplace, he will press the emergency button (or pull a magnetic cord) placed in the smart safety belt which will not only stop the vehicles within 15 meters circle but also inform the workers within 250 meters circle through an indicator. In this technical paper, the outcome of first design phase has been reported. In the first design phase, we have successfully implemented a reliable and accurate ranging mechanism with ±0.3m maximum error. This ranging mechanism is based on wellknown RF Time-of-Flight (TOF) method. The maximum achievable transmit range of this solution is 300m.
Portable gadgets (tablets and smart-phones) provide rapid access to electronic resources and have the potential to improve clinical practice and academic activities. In the present study, we assessed knowledge, practices and perceptions of health-care professionals regarding portable gadgets. Methods and Materials: A questionnaire-based, cross-sectional study was performed on 100 health-care professionals working in the Department of Radiology at Aga Khan University Hospital, Karachi, Pakistan. Sampling methodology was convenience-based and a self-administered questionnaire was used as the instrument for data collection. Items in the study instrument pertained to use of portable gadgets, knowledge of radiology applications and perceptions regarding potential benefits or drawbacks of using such gadgets.
Wideband noise cancelling LNAs reported in recent literature consume very large power because of theadditional stages required for noise cancelling. This makes them inappropriate for portable applications.Based on this fact, the design of a low power, noise cancelling, wideband LNA is presented in this paper.To demonstrate the proposed circuit, novel use of popular current reuse LNA architecture in combinationwith the noise cancellation technique is presented. The current reuse architecture aids in obtaining neces-sary phase difference between signal and noise while providing power optimization. This paper presentsdetailed mathematical analysis of the modified input impedance, gain and noise figure. The proposed LNAis designed for UWB applications using 130 nm IBM CMOS process. The simulation results demonstrated3 dB bandwidth of 2.35–9.37 GHz with maximum forward gain (S21) of 10.3 dB and achieved minimumnoise figure (NFmin) of 3.68 dB. The simulated input referred third order intercept point (IIP3) and 1 dBcompression point (P1dB) are found to be −4 dBm and −12.55 dBm respectively. With a power supply of1.3 V, the proposed circuit consumes 9.97 mW only
This paper presents a low-power noise-matched fully-differential common-gate (CG) low noise amplifier (LNA) for ultrawideband receiver operating in the full 3.1–10.6 GHz band. Performance was optimized by employing the transconductance ‘g m ’ boosted CG LNA topology with series peaking along with an input noise matching network. A common source g m -boosting amplifier, in conjunction with an LC T-network, was used to share the bias current with the CG stage. The LNA was demonstrated using a 130 nm IBM CMOS process technology and it consumed 7 mW from a 1 V supply. It exhibited an input return loss (S11) and an output return loss (S22) of −10.5 and −14 dB respectively. In addition, it also achieved a forward power gain (S21) of 14.5 dB and a noise figure between 4.5 and 5.0 dB.
This paper presents a low-power CMOS transconductance “$g_{m}$” boosted common gate (CG) ultrawideband (UWB) low noise amplifier (LNA) architecture, operating in the 3–5 GHz range, employing current-reuse technique. This proposed UWB CG LNA utilizes a common source (CS) amplifier as the $g_{m}$-boosting stage which shares the bias current with the CG amplifying stage. A detailed mathematical analysis of the LNA is carried out and the different design tradeoffs are analyzed. The LNA circuit was designed and fabricated using the 130-nm IBM CMOS process and it achieved input return loss $({\rm S}_{11})$ and output return loss $({\rm S}_{22})$ variations of respectively $-$ 8.4 to $-$ 40 dB and $-$ 14 to $-$ 15 dB within the pass-band. The LNA exhibits almost flat forward power gain $({\rm S}_{21})$ of 13 dB and a reverse isolation $({\rm S}_{12})$ variation of $-$55 dB to $-$40 dB, along with a noise figure (NF) ranging between 3.5 and 4.5 dB. The complete circuit (with output buffer) draws only 3.4 mW from a 1 V supply voltage.
An improved low noise amplifier (LNA) architecture is presented for WiMedia ultrawideband radio frequency frontend.The LNA topology addresses the issue of noise reduction keeping the power consumption to a minimum by employing the transconductance “gm” boosted common gate (CG) LNA topology with series peaking, operating in the WiMedia spectrum. This CG LNA utilizes an active gm-boosting stage where the bias current is shared between the gm-boosting stage and the self-biased CG amplifying stage. In conjunction with an LC T-network to further reduce the noise figure (NF) of the CG stage with finite output conductance (gds), inductive series peaking is used to widen the operating pass-band.
A low-power noise-matched fully-differential common-gate (CG) low-noise amplifier (LNA) is presented for ultra-wideband (UWB) operating in the 3.1-10.6 GHz band. Performance was optimised by employing the transconductance 'gm'-boosted CG LNA topology with series peaking along with an input noise matching network. A common source gm-boosting amplifier, in conjunction with an LC T-network, was used to share the bias current with the CG stage. The LNA was demonstrated using 130 nm CMOS process and consumed 7 mW from a 1 V supply. It exhibited an input return loss (S11) and an output return loss (S22) of -10.5 dB and -14 dB, respectively. Also, it achieved a forward power gain (S21) of 14.5 dB and a noise figure between 4.5 and 5.0 dB.
This paper focuses on the design optimization of gm-boosted common gate (CG) CMOS low-noise amplifier (LNA) for ultra-wideband (UWB) wireless technology. In this regard, a detailed novel analysis of the UWB gm-boosted CG amplifier topology is presented, which includes the finite gds (=1/reds) effects. For UWB systems, signal-to-noise ratio (SNR) can be defined as the matched filter bound (MFB). Using this definition, the noise performance of the UWB CG LNA in the presence of the gm-boosting gain and the input noise-matching network are analyzed. It is found that the optimal noise factor of the UWB LNA collapses to the published narrowband gm-boosted CG LNA noise factor when an assumption of narrowband is applied. It is also proved that the noise performance of the gm-boosted UWB CG LNA is independent of the bandwidth of the input UWB signal. A new technique is presented for the design of optimal noise-matching network using passive components at the input of the UWB CG LNA. In this regard, role of the gm-boosting stage and its effect on the SNR and the gain of the overall system are analyzed, and, in addition, its non-idealities are simulated in detail.
A novel architecture is presented to optimize the noise performance and the power consumption of the transconductance ‘gm’ boosted common-gate (CG) ultrawideband (UWB) low-noise amplifier (LNA), operating in the 3–5 GHz range, by employing current reuse technique. This proposed CG LNA utilizes a common source (CS) amplifier as the gm-boosting stage and the bias current is shared between the gm-boosting stage and the CG amplifying stage. The LNA circuit also utilizes the short channel conductance gds in conjunction with an LC T-network to further reduce the noise figure (NF). The proposed LNA architecture has been fabricated using the 130 nm IBM CMOS process. The LNA achieved input return loss (S11) of −8 to −10 dB, and, output return loss (S22) of −12 to −14 dB, respectively. The LNA exhibits almost flat forward voltage gain (S21) of 13 dB, and reverse isolation (S12) of −62 to −49 dB, with a NF ranging between 3.8 and 4.6 dB. The measurements indicate an input-referred third order intercept point (IIP3) of −6.1 dBm and an input-referred 1-dB compression point (ICP1dB) of −15.4 dBm. The complete chip draws 4 mW of DC power from a 1.2 V supply.
Currently, fire detection systems based on computer vision techniques are highly appreciated for their intelligent detections at earliest. These systems use surveillance cameras to capture high-level information from a fire that enables a system to take preventive and corrective measures before the happening of a fire hazard. Handling false fire detection and reducing false alarm in such developed systems are still big challenges that need to be addressed. In this paper, a novel framework is proposed that uses angular and regional area information of the fire flame to predict the existence of a fire flame in sequence of a video frames.
Leucocytes, especially neutrophils featuring pro- and anti-cancerous characteristics, are involved in nearly every stage of tumorigenesis. Phenotypic and functional differences among mature and immature neutrophil fractions are well reported, and their correlation with tumor progression and therapy has emerging implications in modern oncology practices. Technological advancements enabled modern hematology analyzers to generate extended information (research parameters) during complete blood cell count (CBC) analysis. We hypothesized that neutrophil and lymphocyte fractions-related extended differential leucocytes count (DLC) parameters hold superior diagnostic utility over routine modalities.
Purpose Breast cancer has caused more deaths in women compared to any other cancer that might be found among women. With that being said, this research has proposed a method which can detect classify and segment the different types of breast tumors. This paper has also discussed the different methods by which the breast cancer has been classified and segmented in the past. Method Breast cancer can be detected in its early stages by MRI and/or mammography of the breast muscles. For this research a novel approach is proposed for breast cancer detection, classification and segmentation. The proposed framework uses breast mammograms from the CBIS-DDSM (Curated Breast Imaging Subset of DDSM) DICOM images. Mammograms are radio images of a muscle.
The evolution of internet to the Internet of Things (IoT) gives an exponential rise to the data collection process. This drastic increase in the collection of person's private information represents a serious threat to his/her privacy. Privacy Preserving Data Publishing (PPDP) is an area that provides a way of sharing data in their anonymized version, i.e. keeping the identity of a person undisclosed. Various anonymization models are available in the area of PPDP that guard privacy against numerous attacks. However, selecting the optimum model which balances utility and privacy is a challenging process. This study proposes an Efficient Data Anonymization Model Selector (EDAMS) for PPDP which generates an optimized anonymized dataset in terms of privacy and utility.
Multiple Sclerosis (MS) is a complex autoimmune neurological disease affecting the myelin sheath of the nerve system. In the world, there are about 2.5 million patients with MS, in South and East Asia the ratio of MS is high. This disease affects young and middle-aged people. The MS is a fatal disease, and the numbers and volumes of MS lesions can be used to determine the degree of disease severity and track its progression. The detection of multiple sclerosis is a critical problem in MRI images because MS is described as frequently involves lesions, it can be appeared on a scan at one time-point and not appeared in subsequent time points. Also, MS on the T2 FLAIR MRI image is more often manifested by the presence of focal changes in the substance of the brain and spinal cord, which complicate their dynamic control according to MRI data.
The evolution of internet to the Internet of Things (IoT) gives an exponential rise to the data collection process. This drastic increase in the collection of person's private information represents a serious threat to his/her privacy. Privacy Preserving Data Publishing (PPDP) is an area that provides a way of sharing data in their anonymized version, i.e. keeping the identity of a person undisclosed. Various anonymization models are available in the area of PPDP that guard privacy against numerous attacks. However, selecting the optimum model which balances utility and privacy is a challenging process. This study proposes an Efficient Data Anonymization Model Selector (EDAMS) for PPDP which generates an optimized anonymized dataset in terms of privacy and utility. EDAMS inputs the dataset with required parameters and produces its anonymized version by incorporating PPDP techniques while balancing utility and privacy. EDAMS is currently incorporating three PPDP techniques, namely k-anonymity, l-diversity, and t-closeness. It is tested against different variations of three datasets. The results are validated by testing each variation explicitly with the stated techniques. The results show the effectiveness of EDAMS by selecting the optimum model with minimal effort.
PurposeMedical imaging is a novel research area in the domain of image processing for the research community. Features computed from MRI images provide a high level of information used in medical diagnostics. This paper addresses the classification of different types of brain tumors studied in MRI images using feature extraction techniques. It may help in the effectiveness of brain tumor treatment that depends on the early detection needed to distinguish between benign and malignant tumors.Method We present in this paper, a novel framework to investigate and classify brain tumors in DICOM format T2-FLAIR MRI images. Spatial filters are used to remove undesired information and noises. Segmentation is done using a thresholding method to separate the tumorous regions from healthy regions. Then, the Discrete Wavelet Transform is employed for reducing the dimensionality of the images followed by Principal Component Analysis, which reduces the dimensions further while keeping the only useful information.
The Phonocardiography is a graphical method of recording of the tones and noise generated by the heart with the help of the phonocardiogram machine. Cardiovascular disease (CVD) and heart failure (HF) are considered life-threatening and mostly cause death. The phonocardiograph signal (PCG) considers an indicator of abnormalities in the cardiovascular system. It provides the ability to carry out qualitative information and quantitative analysis of different tones and heart murmurs. PCG plays a major role in treatment, diagnosis and decision making of the clinical examination and biomedical research fields. The use of simple stethoscope for diagnosis the heart problem requires an experienced physician or doctors. Many people with CVD and HF are dying every day because of the lack of facilities that analysis the heart defects.
Polyurethane (PU) materials are extensively used in the construction industry as core material insulation for sandwich panel application, such as wall cladding, structure insulation and roof panel. Demands on PU application have escalated given its significant advantages to reduce energy consumption. Meanwhile, rigid PU (RPU) foams are combustible materials characterized by rapid flame spread, high heat-release rates, and ability to produce large quantities of toxic gases in original form. Thus, flame-retardant (FR) additives are used to improve the thermal properties of PU. However, some commercial additives used today have are hazardous to humans and the environment, and their extensive application is limited by their negative effects on polymer mechanical properties.
Development of lightweight materials becomes essential and has been applied for various structural and functional applications in industrial field since last decade. Porous metal can contribute to lightweight material with great mechanical, thermal and electrical properties. In this study, porous stainless steel was fabricated by using powder metallurgy technique and egg shell as a new potential space holder material. Stainless steel 316L was used as metal matrix powder, egg shells as space holder material, and polyethylene glycol (PEG) as binder to increase the green density of the preforms.
Human computer interaction has got great demand these days especially if it's serving for the society, consider the best use of technology. Computer vision is also playing a vital role for especial people to interact with the common people of the society. This paper envisages a system that uses Leap Motion device and computer vision techniques to recognize written words in air gestured by human hand. The framework is not only helpful for children to practice writing with fun but it is also useful for the dumb and handy people to communicate with other people. The leap motion device captures hand gestures, track the movements and send the frames to the system. After pre-processing input data, geometric strokes feature are extracted to recognize the written words in air. The recognized text is then display on the computer screen. The test results are out perform for English and Numeric characters with an average accuracy more than 90%.
Malaria in human is a serious and fatal tropical disease. This disease results from Anopheles mosquitoes that are infected by Plasmodium species. The clinical diagnosis of malaria based on the history, symptoms and clinical findings must always be confirmed by laboratory diagnosis. Laboratory diagnosis of malaria involves identification of malaria parasite or its antigen / products in the blood of the patient. Manual diagnosis of malaria parasite by the pathologists has proven to become cumbersome. Therefore, there is a need of automatic, efficient and accurate identification of malaria parasite. In this paper, we proposed a computer vision based approach to identify the malaria parasite from light microscopy images. This research deals with the challenges involved in the automatic detection of malaria parasite tissues. Our proposed method is based on the pixel-based approach. We used K-means clustering (unsupervised approac
Medicinal images assume to be as a key part in diagnosing tumor. MRI scans are the cutting edge restorative imaging technology, which permits cross sectional perspective of human body that gives simplicity to specialists to inspect the patients. An MRI scan is an amalgamation of radio frequency (RF) and gradient pulses that may be used to compute pixel level features to form the image. The pixel level features in MRI images can be used to recognize the minor changes of structures inside the body. In this paper, we proposed a technique to classify tumor cells in brain using MRI scan images of brain by computing (pixel level) HoG features. The tumor cells detection and grading processes are based on the properties of the tissues at cellular level. We train a HoG detector to classify tumor cells in MRI images of brain. Experimental results confirm that the proposed framework offers a promising solution for classifying tumor cells in different MRI images.
Medicinal images assume to be as a key part in diagnosing tumor. MRI scans are the cutting edge restorative imaging technology, which permits cross sectional perspective of human body that gives simplicity to specialists to inspect the patients. An MRI scan is an amalgamation of radio frequency (RF) and gradient pulses that may be used to compute pixel level features to form the image. The pixel level features in MRI images can be used to recognize the minor changes of structures inside the body. In this paper, we proposed a technique to classify tumor cells in brain using MRI scan images of brain by computing (pixel level) HoG features. The tumor cells detection and grading processes are based on the properties of the tissues at cellular level. We train a HoG detector to classify tumor cells in MRI images of brain. Experimental results confirm that the proposed framework offers a prom.
Malaria in human is a serious and fatal tropical disease. This disease results from Anopheles mosquitoes that are infected by Plasmodium species. The clinical diagnosis of malaria based on the history, symptoms and clinical findings must always be confirmed by laboratory diagnosis. Laboratory diagnosis of malaria involves identification of malaria parasite or its antigen/products in the blood of the patient. Manual diagnosis of malaria parasite by the pathologists has proven to become cumbersome. Therefore, there is a need of automatic, efficient and accurate identification of malaria parasite. In this paper, we proposed a computer vision based approach to identify the malaria parasite from light microscopy images. This research deals with the challenges involved in the automatic detection of malaria parasite tissues. Our proposed method is based on the pixel based approach. We used K-means clustering (unsupervised approach) for the segmentation to identify malaria parasite tissues.
The illiteracy rate in Pakistan is sky-scraping as compare to the other developed countries of the world. Being illiterate is not only a single person disability; it also has some social implications. It is very hard for the democratic institutions and values to prosper in a society where half of the adult population is uneducated, and most of the citizen cannot access information or read newspapers. The situation seems to be more critical when a large number of students drop their education in a mid-way because of some factors. Pakistan is one of the countries of the world in which the illiterate peoples are great in numbers. This study was conducted to examine these factors as well as the basic reasons which have some influence on the academic performance of the students belongs to higher secondary education especially in big cities of Pakistan like in Karachi.
This paper proposes an unsupervised learning framework in which models of multiple objects� appearance classes are learned from video. These models are used to detect objects of different classes in the scene. The proposed technique combines appearance and motion features in a weighted combination framework resulting in models of object classes. Thus, better detection results are achieved compared to foreground based tracking and to those obtained in a supervised way. Since the proposed technique is unsupervised, a good detection rate is achieved without manual effort expended in data collection and labelling. Experimental results confirm that the proposed framework offers a promising solution for detection in unfamiliar scenes.
This paper proposes an unsupervised learning framework in which models of objects' appearance classes are learned using their spatio and temporal information, from video. These models are used to detect objects of different classes in the everyday scene. The proposed technique combines appearance and motion features in a weighted combination framework resulting in models of object classes. Thus, better detection results are achieved compared to foreground based tracking and to those obtained in a supervised way. Since the proposed technique is unsupervised, a good detection rate is achieved without manual effort expended in data collection and labelling. Experimental results confirm that the proposed framework offers a promising solution for detection in unfamiliar scenes.
In this paper we present a computer vision system for real time detection of moving objects and predict their activity on identifYing their size, position and number of dynamic objects in a frame by capturing video through surveillance system. The system capture video of a scene segments it and identifies moving object (s) by subtracting static objects. In the first phase segmenting video frame the system uses descriptor for identifying the dynamic objects from image sequence recorded by the video camera. In the second phase the system uses Kalman filter and predicts the next movement of the object of interest if it involves some suspicious activity.
Non-volatile nanoscale memory devices (such as memristors) have promised to overcome the challenges of scalability and leakage currents of CMOS based memory devices. These novel memories can be fabricated in back-end-of-the-line of any CMOS process. Currently, a lot of research is focused on investigating the benefits of memristors for associative memories. These are Content-Addressable Memories (CAM) in which search based data access takes place. Searching for a particular bit in memristor is time consuming while search in CMOS CAM zone is efficient. To combine the speed and ease of search of CMOS memory and the scalability of memristor memory, we present a novel multibit hybrid CMOS-Memristor Associative Memory Cell.
High Efficiency Video Coding (HEVC/H.265) is an emerging standard for video compression that provides almost double compression efficiency at the cost of major computational complexity increase as compared to current industry-standard Advanced Video Coding (AVC/H.264). In HEVC more flexible coding options and partitioning types are provided for prediction units (PUs) and consequently motion estimation (ME) is more involved than previous video-coding standards. The existing fast ME algorithms, including the test zone search provided in the HEVC reference software, cannot generate high-quality video with reasonable computational complexity. In this paper, four algorithms are proposed and designed for optimizing the motion estimation process for the H.265/HEVC whilst maintaining the same quality and the compression rate as the standard. The conducted experiments show significant speed improvements, thus making a novel contribution to the implementation of realtime H.265 standard encoders in computationally constrained environments such as low-power mobile devices and general purpose computers.
With the scaling of feature sizes into Deep-Submicron (DSM) values and ever increasing operating frequencies, chip failures due to crosstalk noise have become a major concern for circuit designers. It is essential that these faults be accurately diagnosed and affected wires re-routed to avoid possible chip failures. In this paper a new diagnostic fault simulator is described that diagnoses crosstalk induced glitch and delay faults in combinational circuit using information from fault simulation of single stuck-at faults. A realistic crosstalk fault model is used to insert crosstalk glitch and delay effects on a victim wire. The model considers the logical values of all possible time-compatible aggressors with those of the victim to insert the various noise effects. Once the design is found to be defective potential candidate wires are shortlisted by considering the fan-in cone of the faulty outputs. Further localization of fault site is performed by considering the logic states of candidate wires and their aggressors for the applied pattern pair. Experimental results on ISCAS'85 benchmark circuit for the given approach show that the method achieves high accuracy in localizing the crosstalk fault sites with good diagnostic resolution.
Crosstalk noise due to parasitic couplings between two closely located neighboring wires has significant impact on the performance of the high speed DSM chips. Analysis of crosstalk effect using a single wire with all of its coupling parameters is much easier and very convenient for determining the maximum effect of the crosstalk noise both in terms of glitch and delay. With this objective, in this paper a decoupled and distributed RLGC transient model of the victim wire is introduced which takes into account all coupling effects and is very fast, highly flexible and yet accurate enough. Using this decoupled victim model also some analytical or numerical approaches for determining the critical values of influencing parameters can be developed. The efficacy of the decoupled victim model is also compared with the coupled two interconnects' PSPICE and MATLAB simulation results, which show comparable performance for the model's accuracy but significantly superior performance for simulation speed.
VLSI circuits have become more susceptible to signal integrity related failures with the ever decreasing process geometries. Detection of crosstalk induced faults is thus important as capacitive crosstalk is one of the major sources of signal integrity related failures. Crosstalk glitch can result in erroneous output if the glitch effect propagates to a primary output or to an intermediate flip-flop. Similarly the crosstalk induced delay effects can also result in latching of an incorrect value if the delay exceeds the allowed margins. In this work a test generation and compaction method is proposed for crosstalk faults. Test patterns are generated by simultaneously considering the coupling capacitance, timing and functional incompatibilities between the victim and aggressor nets, to produce the practical maximum crosstalk noise. A unique method is proposed for finding the functional incompatibilities between interconnects. The generated test set is then compacted initially through pattern merging and then further through the fault-chaining algorithm. Three different implementations of this algorithm are compared on crosstalk test sets generated for ISCAS'85 benchmark circuits. Results show considerable reduction in crosstalk pessimism for the given layout and timing, as well as up to 75% reduction in overall test set size.
Noise effects due to parasitic couplings between two closely located neighboring wires have significant impact on the performance of the DSM chips. Analysis of a single wire with all its couplings is required to find out the maximum effect of the crosstalk noise both in terms of glitch and delay. This paper introduces a decoupled RLGC transient model for victim wire which is highly accurate and flexible. This model can be used to compute the maximum delay and glitch effect due to crosstalk on a victim wire under different slew rates and delays of aggressor and victim signals. The equivalent victim model's accuracy is validated by the PSPICE simulation results and yet the simulation speed is at least 13 times faster than the latter.
Detection of crosstalk induced glitch faults is important as they can result in erroneous output if the glitch effect propagates to a primary output or to an intermediate flip-flop. A new method is thus presented in this paper to generate test patterns for crosstalk induced glitch faults followed by compaction of the test set. By considering the spatial and temporal aspects between the victim and neighboring interconnects and further by considering the functional incompatibilities between individual aggressors, test patterns are generated which produce the maximal crosstalk effect on victim net. Compaction of the test set is achieved initially by merging compatible test patterns and subsequently through sequencing of test patterns of the fault list. Experimental results on ISCAS'85 benchmark circuits demonstrate that up to 78% reduction of test patterns can be achieved.
Detection of crosstalk induced glitch faults is important as they can result in erroneous output if the glitch effect propagates to a primary output or to an intermediate flip-flop. A new method is thus presented in this paper to generate test patterns for crosstalk induced glitch faults followed by compaction of the test set. By considering the spatial and temporal aspects between the victim and neighboring interconnects and further by considering the functional incompatibilities between individual aggressors, test patterns are generated which produce the maximal crosstalk effect on victim net. Compaction of the test set is achieved initially by merging compatible test patterns and subsequently through sequencing of test patterns of the fault list. Experimental results on ISCAS'85 benchmark circuits demonstrate that up to 78% reduction of test patterns can be achieved.
Noise effects in coupled interconnects, i.e. crosstalk induced glitch and crosstalk induced delay can significantly impact the performance of deep sub-micron (DSM) chips. Therefore, in this paper distributed RLGC transient model of coupled interconnects has been developed that will be useful for analyzing such crosstalk noise effects in DSM chips. The model accuracy is quite comparable to the PSPICE simulation results and yet the simulation speed is at least 11 times faster than the latter.
This work proposes a TPG method for producing maximal crosstalk glitch effect on victim net by considering the spatial, temporal and functional properties of the circuits. The generated test set is then compacted initially through pattern merging and then through fault-list chaining algorithm. Finally different approaches to this algorithm are implemented for comparison.
This work proposes a TPG method for producing maximal crosstalk glitch effect on victim net. Thereafter, the test set is compacted using different implementations of fault-list chaining algorithm.