Interconnecting networks with optimized service provisioning

Telecommunication Systems - A recent trend of peering at geo-diversified Internet exchange points (IXPs) has empowered decades-old proposal of inter-networking and opened up new avenues of business...     

Extended Virtual Boundary and Its Application in Dynamic Spectrum Allocation

This paper presents an efficient dynamic spectrum allocation （DSA） scheme in a flexible spectrum licensing environment where multiple networks coexist and interfere with each other. In particular, an extension of virtual boundary concept in DSA is proposed, which is spectrally efficient than the previous virtual boundary concept applied to donor systems only. Here, the same technique is applied to both donor and rental systems so as to further reduce the occurrences where the insertion of guard bands is obligatory and as a result provides better spectral efficiency. The proposed extension improves the spectrum utilization without any compromise on interference and fairness issues.     

Novel Approach for Modeling Wireless Fading Channels Using a Finite State Markov Chain

Empirical modeling of wireless fading channels using common schemes such as autoregression and the finite state Markov chain (FSMC) is investigated. The conceptual background of both channel structures and the establishment of their mutual dependence in a confined manner are presented. The novel contribution lies in the proposal of a new approach for deriving the state transition probabilities borrowed from economic disciplines, which has not been studied so far with respect to the modeling of FSMC wireless fading channels. The proposed approach is based on equal portioning of the received signal‐to‐noise ratio, realized by using an alternative probability construction that was initially highlighted by Tauchen. The associated statistical procedure shows that a first‐order FSMC with a limited number of channel states can satisfactorily approximate fading. The computational overheads of the proposed technique are analyzed and proven to be less demanding compared to the conventional FSMC approach based on the level crossing rate. Simulations confirm the analytical results and promising performance of the new channel model based on the Tauchen approach without extra complexity costs.     

Finite volume based CFD simulation of pressurized flip-chip underfill encapsulation process

This paper presents the simulation of pressurized underfill encapsulation process for high I/O flip chip package. 3D model of flip chip packages is built using GAMBIT and simulated using FLUENT software. Injection methods such as central point, one line, L-type and U-type are studied. Cross-viscosity model and volume of fluid (VOF) technique are applied for melt front tracking of the encapsulant. The melt front profiles and pressure field for all injection types are analyzed and presented. The pressure distribution within the flip-chip, fill volume versus filling time and viscosity versus shear rate are also plotted. The U-type injection is found to be faster in filling. The numerical results are compared with the previous experimental results and found in good conformity. The strength of CFD software in handling underfill encapsulation problems is proved to be excellent.     

Interaction of α radiation with iron-doped n-type silicon

Deep-level transient spectroscopy (DLTS) measurements were carried out on low-doped n-silicon before and after irradiation with 5.48 MeV α particles at room temperature with a fluence of 10¹⁰ α particles/cm². The DLTS measurements on the samples identified three electron levels E₁, E₂ and E₃ before irradiation. The deep-levels characteristic studies include emission rate signatures, activation energies, defect concentrations and capture cross sections. It was found that all pre-existing defects decreased their amplitudes during irradiation. The decrease in activation energy of level E₃ and noticeable suppression of level E₁ was also observed after irradiation. It was clearly seen that the composite peak E₃ (combination of E₂ and E₃) was successfully resolved after irradiating with α particles. α-irradiation is seen to lead a significant suppression of the iron interstitial defect, and without causing any change in its room temperature annealing characteristics.     

Design considerations of all-optical A/D conversion: nonlinear fiber-optic Sagnac-loop interferometer-based optical quantizing and coding

The authors describe in detail the design considerations of our previously proposed novel optical quantizing and coding method for all-optical analog-to-digital (A/D) conversion using nonlinear optical switches based on the Sagnac interferometer. The multiperiod transfer function, which is the key to quantizing and coding, is achieved through a careful design of the Sagnac interferometer. In the experiments, the intensity of the pulse train input to our A/D converter is manually changed, and the corresponding digital signals are successfully mapped generated. Although the input-pulse trains are not the sampling of real analog signal, the principle of our proposed 3-bit A/D conversion at a 10 gigasample per second (Gsps) rate is demonstrated. The proposed optical quantizing and coding, combined with existing optical sampling techniques, will enable ultrafast photonic A/D conversion without electronics. The potential in the frequency regime of over a few hundred gigasamples per second was investigated by using an optical switch that utilizes the optical Kerr effect for fast operation. It was found out that the wavelength allocations and temporal widths of control and probe pulses have to be optimized with respect to the group-velocity dispersion of highly nonlinear fiber.     

An Enhanced A-MSDU Frame Aggregation Scheme for 802.11n Wireless Networks

The main goal of the IEEE 802.11n standard is to achieve a minimum throughput of 100 Mbps at the MAC service access point. This high throughput has been achieved via many enhancements in both the physical and MAC layers. A key enhancement at the MAC layer is frame aggregation in which the timing and headers overheads of the legacy MAC are reduced by aggregating multiple frames into a single large frame before being transmitted. Two aggregation schemes have been defined by the 802.11n standard, aggregate MAC service data unit (A-MSDU) and aggregate MAC protocol data unit (A-MPDU). As a consequence of the aggregation, new aggregation headers are introduced and become parts of the transmitted frame. Even though these headers are small compared to the legacy headers they still have a negative impact on the network performance, especially when aggregating frames of small payload. Moreover, the A-MSDU is highly influenced by the channel condition due mainly to lack of subframes sequence control and retransmission. In this paper, we have proposed an aggregation scheme (mA-MSDU) that reduces the aggregation headers and implements a retransmission control over the individual subframes at the MSDU level. The analysis and simulations results show the significance of the proposed scheme, specifically for applications that have a small frame size such as VoIP.     

Compressive sensing based high-resolution passive bistatic radar

Passive bistatic radar (PBR) systems using different communication signals can only offer low-resolution target detection due to their inherent low bandwidth. In this paper, compressive sensing (CS) is applied to multichannel FM and GSM PBR to achieve improved range-Doppler resolutions and avoid some limitations of classical multiband PBR processing. In CS context, block-structured time-domain dictionary which is formed from multichannel signals suffers from coherence when fine range resolution is employed. To overcome such a pitfall, this work first transforms the dictionary to spectral domain where only the most important spectral components are retained. Principle component analysis followed by a whitening method are then applied to this spectrally transformed data in order to reduce the dimensionality of problem, thereby reducing the dictionary size and most importantly fulfilling the required condition of dictionary incoherence for better CS-based recovery. Two different block-structured dictionary formations are tested. The performance of the recovery of spatially close targets, in both FM and GSM PBR setups, are reported.     

Biomimetic Nanomaterial Strategies for Virus Targeting: Antiviral Therapies and Vaccines

The ongoing coronavirus disease 2019 (COVID-19) pandemic highlights the importance of developing effective virus targeting strategies to treat and prevent viral infections. Since virus particles are nanoscale entities, nanomaterial design strategies are ideally suited to create advanced materials that can interact with and mimic virus particles. In this progress report, the latest advances in biomimetic nanomaterials are critically discussed for combating viral infections, including in the areas of nanomaterial-enhanced viral replication inhibitors, biomimetic virus particle capture schemes, and nanoparticle vaccines. Particular focus is placed on nanomaterial design concepts and material innovations that can be readily developed to thwart future viral threats. Pertinent nanomaterial examples from the COVID-19 situation are also covered along with discussion of human clinical trial efforts underway that might lead to next-generation antiviral therapies and vaccines.     

A comprehensive survey of AI-enabled phishing attacks detection techniques

In recent times, a phishing attack has become one of the most prominent attacks faced by internet users, governments, and service-providing organizations. In a phishing attack, the attacker(s) collects the client’s sensitive data (i.e., user account login details, credit/debit card numbers, etc.) by using spoofed emails or fake websites. Phishing websites are common entry points of online social engineering attacks, including numerous frauds on the websites. In such types of attacks, the attacker(s) create website pages by copying the behavior of legitimate websites and sends URL(s) to the targeted victims through spam messages, texts, or social networking. To provide a thorough understanding of phishing attack(s), this paper provides a literature review of Artificial Intelligence (AI) techniques: Machine Learning, Deep Learning, Hybrid Learning, and Scenario-based techniques for phishing attack detection. This paper also presents the comparison of different studies detecting the phishing attack for each AI technique and examines the qualities and shortcomings of these methodologies. Furthermore, this paper provides a comprehensive set of current challenges of phishing attacks and future research direction in this domain.